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PROCESS SYSTEMS ENGINEERING Global Optimization of Mixed-Integer Nonlinear

Global Optimization of Mixed-Integer Nonlinear
C. S. Adjiman, I. P. Androulakis, and C. A. Floudas
Dept. of Chemical Engineering, Princeton University, Princeton, NJ 08544
Two no容l deterministic global optimization algorithms for noncon容x mixed-integer
( )problems MINLPs are proposed, using the ad家nces of the BB algorithm for non-
con容x NLPs of Adjiman et al. The special structure mixed-integer BB algorithm
( )SMIN- BB addresses problems with noncon容xities in the continuous 家riables and
linear and mixed-bilinear participation of the binary 家riables. The general structure
( )mixed-integer BB algorithm GMIN- BB is applicable to a 容ry general class of
problems for which the continuous relaxation is twice continuously differentiable. Both
algorithms are de容loped using the concepts of branch-and-bound, but they differ in
their approach to each of the required steps. The SMIN- BB algorithm is based on the
con容x underestimation of the continuous functions, while the GMIN- BB algorithm
is centered around the con容x relaxation of the entire problem. Both algorithms rely on
optimization or inter家l-based 家riable-bound updates to enhance efficiency. A series of
medium-size engineering applications demonstrates the performance of the algorithms.
Finally, a comparison of the two algorithms on the same problems highlights the 家lue
of algorithms that can handle binary or integer 家riables without reformulation.


Source: Androulakis, Ioannis (Yannis) - Biomedical Engineering Department & Department of Chemical and Biochemical Engineering, Rutgers University


Collections: Engineering; Biology and Medicine